How does this work technically?
The first, and crucial, step in this type of analysis is defining a goal, something that is important to you or your organization such as ROI, conversion rate, downtime, etc. Trendskout needs this information to perform evaluations to learn what drives these goals. This can be done directly in the Trendskout UI based on your data, with no annotation.
Unlike traditional systems, Trendskout can evaluate multiple types of data simultaneously. This is not only a technical advantage, but also ensures that you can expand your original data with all kinds of other data sources that can be evaluated for relationships. The original data, in which you selected your target, is expanded with other data that you upload. This allows you to examine on a very broad scale what drives your goals, without missing any connection. One of the technological pillars of Trendskout is a distributed computing platform, with a high degree of parallelization. This technology is used to process, denormalize, clean up and transform the different data sources into other formats so that they can be processed by neural networks and other Deep Learning techniques in Trendskout.
Deep Propensity Modelling
Propensity Modeling is a technique that has been used by statisticians for several decades. The problem with these classical techniques was often that the discovered connections could not be properly described by static, mathematical formulas. Due to new developments in the field of Deep Learning, these relationships can now be modeled in a much more powerful way. By way of illustration you can compare modeling with purely mathematical formulas with trying to draw a face with only straight lines, the result will be angular and only a rough indication of that person’s appearance. Deep Learning techniques can also draw smooth lines, and will therefore paint a better picture. This is also what happens with Deep Propensity Modeling, the relationships in your data will be better understood by neural networks. During the Deep Propensity Modeling step, Trendskout applies various types of Deep Learning algorithms to your data, and it is continually evaluated whether the discovered connections and insights actually have an impact on your goal. For defining your goal in the first step and the data expansion afterwards, no interaction is required for this. As with other AI and Deep Learning analyzes in Trendskout, Auto ML & Solution Space Exploration – data processing, algorithm selection, and parameter hypertuning – automatically searches for the most efficient model.
After the Deep Propensity Modeling phase, the underlying relationships are extracted from the winning model. These relationships and results of simulations provide insight into how your business goal is influenced, in a positive or negative way. This report is one of the output options in Trendskout. In addition to direct consultation in Trendskout, the information in this report can also be linked to the business intelligence solution of your organization.